
"""simple docstring"""


import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       torch.FloatTensor
       UpperCamelCase_		:       Optional[torch.FloatTensor]				=      None





def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_=0.999		,   UpperCAmelCase_="cosine"		,   )->				Dict:







       """simple docstring"""
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(UpperCAmelCase_					):
                     return math.cos((t + 0.008) / 1.008 * math.pi / 2					) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(UpperCAmelCase_					):
                     return math.exp(t * -12.0					)

       else:
              raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"					)

       UpperCamelCase       			=		[]
       for i in range(UpperCAmelCase_					):
              UpperCamelCase       			=		i / num_diffusion_timesteps
              UpperCamelCase       			=		(i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(UpperCAmelCase_					) / alpha_bar_fn(UpperCAmelCase_					)		,   UpperCAmelCase_					)					)
       return torch.tensor(UpperCAmelCase_		,   dtype=torch.floataa					)



class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase						):






       @register_to_config
       def __init__(	self			:				Optional[int]							,   UpperCAmelCase_			:				int = 1_000							,   UpperCAmelCase_			:				str = "fixed_small_log"							,   UpperCAmelCase_			:				bool = True							,   UpperCAmelCase_			:				Optional[float] = 1.0							,   UpperCAmelCase_			:				str = "epsilon"							,   UpperCAmelCase_			:				str = "squaredcos_cap_v2"							,   )->	Optional[Any]:







              """simple docstring"""
              if beta_schedule != "squaredcos_cap_v2":
                     raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"      )

              UpperCamelCase       			=		betas_for_alpha_bar(UpperCAmelCase_      )

              UpperCamelCase       			=		1.0 - self.betas
              UpperCamelCase       			=		torch.cumprod(self.alphas							,   dim=0      )
              UpperCamelCase       			=		torch.tensor(1.0      )

              # standard deviation of the initial noise distribution
              UpperCamelCase       			=		1.0

              # setable values
              UpperCamelCase       			=		None
              UpperCamelCase       			=		torch.from_numpy(np.arange(0							,   UpperCAmelCase_      )[::-1].copy()      )

              UpperCamelCase       			=		variance_type






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   UpperCAmelCase_			:				torch.FloatTensor							,   UpperCAmelCase_			:				Optional[int] = None      )->	torch.FloatTensor:







              """simple docstring"""
              return sample






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				Union[str, torch.device] = None      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		num_inference_steps
              UpperCamelCase       			=		(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
              UpperCamelCase       			=		(np.arange(0							,   UpperCAmelCase_      ) * step_ratio).round()[::-1].copy().astype(np.intaa      )
              UpperCamelCase       			=		torch.from_numpy(UpperCAmelCase_      ).to(UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				Optional[int]=None							,   UpperCAmelCase_			:				Any=None							,   UpperCAmelCase_			:				List[str]=None      )->	Union[str, Any]:







              """simple docstring"""
              if prev_timestep is None:
                     UpperCamelCase       			=		t - 1

              UpperCamelCase       			=		self.alphas_cumprod[t]
              UpperCamelCase       			=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
              UpperCamelCase       			=		1 - alpha_prod_t
              UpperCamelCase       			=		1 - alpha_prod_t_prev

              if prev_timestep == t - 1:
                     UpperCamelCase       			=		self.betas[t]
              else:
                     UpperCamelCase       			=		1 - alpha_prod_t / alpha_prod_t_prev

              # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
              # and sample from it to get previous sample
              # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
              UpperCamelCase       			=		beta_prod_t_prev / beta_prod_t * beta

              if variance_type is None:
                     UpperCamelCase       			=		self.config.variance_type

              # hacks - were probably added for training stability
              if variance_type == "fixed_small_log":
                     UpperCamelCase       			=		torch.log(torch.clamp(UpperCAmelCase_							,   min=1e-20      )      )
                     UpperCamelCase       			=		torch.exp(0.5 * variance      )
              elif variance_type == "learned_range":
                     # NOTE difference with DDPM scheduler
                     UpperCamelCase       			=		variance.log()
                     UpperCamelCase       			=		beta.log()

                     UpperCamelCase       			=		(predicted_variance + 1) / 2
                     UpperCamelCase       			=		frac * max_log + (1 - frac) * min_log

              return variance






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]							,   UpperCAmelCase_			:				torch.FloatTensor							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				torch.FloatTensor							,   UpperCAmelCase_			:				Optional[int] = None							,   UpperCAmelCase_			:				int=None							,   UpperCAmelCase_			:				bool = True							,   )->	Union[UnCLIPSchedulerOutput, Tuple]:







              """simple docstring"""
              UpperCamelCase       			=		timestep

              if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                     UpperCamelCase			,							UpperCamelCase       			=		torch.split(UpperCAmelCase_							,   sample.shape[1]							,   dim=1      )
              else:
                     UpperCamelCase       			=		None

              # 1. compute alphas, betas
              if prev_timestep is None:
                     UpperCamelCase       			=		t - 1

              UpperCamelCase       			=		self.alphas_cumprod[t]
              UpperCamelCase       			=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
              UpperCamelCase       			=		1 - alpha_prod_t
              UpperCamelCase       			=		1 - alpha_prod_t_prev

              if prev_timestep == t - 1:
                     UpperCamelCase       			=		self.betas[t]
                     UpperCamelCase       			=		self.alphas[t]
              else:
                     UpperCamelCase       			=		1 - alpha_prod_t / alpha_prod_t_prev
                     UpperCamelCase       			=		1 - beta

              # 2. compute predicted original sample from predicted noise also called
              # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
              if self.config.prediction_type == "epsilon":
                     UpperCamelCase       			=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
              elif self.config.prediction_type == "sample":
                     UpperCamelCase       			=		model_output
              else:
                     raise ValueError(
                         f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
                         " for the UnCLIPScheduler."      )

              # 3. Clip "predicted x_0"
              if self.config.clip_sample:
                     UpperCamelCase       			=		torch.clamp(
                         UpperCAmelCase_							,   -self.config.clip_sample_range							,   self.config.clip_sample_range      )

              # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
              # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
              UpperCamelCase       			=		(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
              UpperCamelCase       			=		alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

              # 5. Compute predicted previous sample µ_t
              # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
              UpperCamelCase       			=		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

              # 6. Add noise
              UpperCamelCase       			=		0
              if t > 0:
                     UpperCamelCase       			=		randn_tensor(
                         model_output.shape							,   dtype=model_output.dtype							,   generator=UpperCAmelCase_							,   device=model_output.device      )

                     UpperCamelCase       			=		self._get_variance(
                         UpperCAmelCase_							,   predicted_variance=UpperCAmelCase_							,   prev_timestep=UpperCAmelCase_							,   )

                     if self.variance_type == "fixed_small_log":
                            UpperCamelCase       			=		variance
                     elif self.variance_type == "learned_range":
                            UpperCamelCase       			=		(0.5 * variance).exp()
                     else:
                            raise ValueError(
                                f"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
                                " for the UnCLIPScheduler."      )

                     UpperCamelCase       			=		variance * variance_noise

              UpperCamelCase       			=		pred_prev_sample + variance

              if not return_dict:
                     return (pred_prev_sample,)

              return UnCLIPSchedulerOutput(prev_sample=UpperCAmelCase_							,   pred_original_sample=UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   UpperCAmelCase_			:				torch.FloatTensor							,   UpperCAmelCase_			:				torch.FloatTensor							,   UpperCAmelCase_			:				torch.IntTensor							,   )->	torch.FloatTensor:







              """simple docstring"""
              # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
              UpperCamelCase       			=		self.alphas_cumprod.to(device=original_samples.device							,   dtype=original_samples.dtype      )
              UpperCamelCase       			=		timesteps.to(original_samples.device      )

              UpperCamelCase       			=		alphas_cumprod[timesteps] ** 0.5
              UpperCamelCase       			=		sqrt_alpha_prod.flatten()
              while len(sqrt_alpha_prod.shape      ) < len(original_samples.shape      ):
                     UpperCamelCase       			=		sqrt_alpha_prod.unsqueeze(-1      )

              UpperCamelCase       			=		(1 - alphas_cumprod[timesteps]) ** 0.5
              UpperCamelCase       			=		sqrt_one_minus_alpha_prod.flatten()
              while len(sqrt_one_minus_alpha_prod.shape      ) < len(original_samples.shape      ):
                     UpperCamelCase       			=		sqrt_one_minus_alpha_prod.unsqueeze(-1      )

              UpperCamelCase       			=		sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
              return noisy_samples







"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)



class 	__a	(   _lowerCAmelCase						):






       def __init__(	self			:				str							,   *UpperCAmelCase_			:				str							,   **UpperCAmelCase_			:				Any      )->	None:







              """simple docstring"""
              warnings.warn(
                  "The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                  " use YolosImageProcessor instead."							,   UpperCAmelCase_							,   )
              super().__init__(*UpperCAmelCase_							,   **UpperCAmelCase_      )





